In this paper, we argue that the agglomerative clustering with vector cosine similarity measure performs poorly due to two reasons. First, the nearest neighbors of a document belong to different classes in many cases since any pair of documents shares lots of "general" words. Second, the sparsity of class-specific "core" words leads to grouping documents with the same class labels into different clusters. Both problems can be resolved by suitable smoothing of document model and using

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... ibler divergence of two smoothed models as pairwise document distances. Inspired by the recent work in information retrieval, we propose a novel context-sensitive semantic smoothing method that can automatically identifies multiword phrases in a document and then statistically map phrases to individual document terms. We evaluate the new model-based similarity measure on three datasets using complete linkage criterion for agglomerative clustering and find out it significantly improves the clustering quality over the traditional vector cosine measure.